Comparar métodos

	Aprendizaje Robusto en Línea	Aprendizaje Online Semi-supervisado
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	2000s–2010s	2000s–2010s
Autor original	Hazan, E.; Shalev-Shwartz, S.; and others	Goldberg, A.; Li, M.; Zhu, X. (among key contributors)
Tipo	Algorithmic framework	Hybrid learning paradigm (online + semi-supervised)
Fuente seminal	Hazan, E. (2016). Introduction to Online Convex Optimization. Foundations and Trends in Optimization, 2(3–4), 157–325.	Goldberg, A., Li, M., & Zhu, X. (2008). Online manifold regularization: A new learning setting and empirical study. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2008), Lecture Notes in Computer Science, 5211, 393–407. Springer.
Alias	ROL, robust incremental learning, adversarially robust online learning, robust sequential learning	SSOL, online semi-supervised learning, semi-supervised incremental learning, streaming semi-supervised learning
Relacionados	5	4
Resumen	Robust Online Learning extends the online learning framework — where a model updates sequentially after each observation — by incorporating robustness mechanisms that guard against corrupted labels, adversarial examples, heavy-tailed noise, and concept drift. The result is a sequential learner that maintains bounded regret even when the data stream contains outliers or deliberate perturbations.	Semi-supervised Online Learning combines the incremental update style of online learning with the ability to exploit unlabeled examples, enabling models to improve continuously from a data stream in which only a small fraction of arriving instances carry ground-truth labels. It is especially valuable when labeling is expensive or delayed but data arrives in real time.